Question 1
Under what circumstance, unlikely as it might be, would the standard error of estimate be zero?
Select one:
a. The correlation coefficient is either 1.00 or -1.00.
b. The proportionate reduction in error is also zero.
c. The correlation coefficient is also zero.
d. The standard error of estimate can never be zero.
Question 2
Every year it seems as though last season’s baseball rookie of the year fails to live up to expectations for his sophomore season. What might explain this phenomenon?
Select one:
a. regression to the mean
b. proportionate reduction in error
c. overestimation of effect size
d. standard error of the estimation
Question 3
With regression we are concerned about variability around the ________, rather than variability around the ________ which would be the case in t tests or ANOVAs.
Select one:
a. outliers; line of best fit
b. median; tails of the distribution
c. mean; outliers
d. line of best fit; mean
Question 4
In a study designed to predict blood cholesterol levels from amount of daily saturated fat in grams (X1) and number of hours of daily exercise (X2), we determine that the slope of X1 is 5, the slope of X2 is -4, and the y intercept is 130. Which of the following formulas is the regression equation for these data?
Select one:
a. ? = 130 + 5(X1) -4(X2)
b. ? = 130 + 5(X1) + 4(X2)
c. ? = 130 + 1(X)
d. ? = 130 -5(X1) -4(X2)
Question 5
We can examine a graph to get a sense of how much error there is in a regression equation. Which of the following describes a graph that reveals there will be a high amount of error when using our regression equation?
Select one:
a. Data points cluster very close to the line with several outlier exceptions.
b. The data points consistently cluster far away from the line of best fit.
c. Data points cluster close around the line of best fit.
d. Data points fall directly on the line.
Question 6
In the equation for a regression line, the slope is the:
Select one:
a. z score of the amount that Y is predicted to increase as X increases.
b. value for X when Y is equal to 0.
c. predicted value for Y when X is equal to 0.
d. amount that Y is predicted to increase for a one-unit increase in X.
Question 7
The statistical analysis that allows us to use one scale variable to predict outcome on a second scale variable is called:
Select one:
a. linear analysis.
b. regression.
c. prediction.
d. correlation.
Question 8
The standard error of the estimate indicates:
Select one:
a. the typical distance between the regression line and each of the observed data points.
b. how much error there is in any single prediction we make from a given regression equation.
c. how far, on average, the regression line is from the mean.
d. how far two regression lines are from each other.
Question 9
An independent variable that makes a unique contribution to the prediction of a dependent variable is a(n) ________ variable.
Select one:
a. latent
b. manifest
c. unique
d. orthogonal
Question 10
A researcher wants to be able to predict first-semester grade point average with as much accuracy as possible, so she would like to use both high school grade point average and SAT score as predictor variables. Which of the following techniques would be most appropriate to make this prediction?
Select one:
a. standardized regression coefficient
b. multiple regression
c. simple linear regression
d. proportionate reduction in error
Question 11
To determine the Y intercept, we determine the value of Y when X is:
Select one:
a. at the minimum of the data set.
b. at the mean.
c. at the maximum of the data set.
d. zero.
Question 12
In regression, the discovery of additional predictor variables that are separate and distinct serves to:
Select one:
a. over-complicate the analysis.
b. help researchers assess error of prediction.
c. explain more of the variability in our outcome variable.
d. create redundancy in our equation.
Question 13
If two variables, independently, can help us predict the outcome of a third variable, we say that they are:
Select one:
a. proportionate.
b. standardized.
c. autonomous.
d. orthogonal.
Question 14
The predicted z score for the dependent variable will always be ________ the individual’s z score for the independent variable.
Select one:
a. less than
b. the same as
c. two times
d. more than
Question 15
As the standard error of estimate becomes larger, predictions become:
Select one:
a. less accurate.
b. smaller.
c. more accurate.
d. larger.
Question 16
The tendency for very tall parents to have children who are somewhat short illustrates the phenomenon:
Select one:
a. variability.
b. regression to the mean.
c. central limit theorem.
d. central tendency.
Question 17
A small standard error of the estimate means that:
Select one:
a. your two variables are poorly correlated.
b. variability is high in your Y variable.
c. confounding variables may be present.
d. you are making predictions with great accuracy.
Question 18
For a simple linear regression, the standardized regression coefficient is:
Select one:
a. the square root of the slope.
b. the square of the r statistic.
c. equal to the Pearson correlation coefficient.
d. unrelated to the correlation value.
Question 19
The regression line is also called the:
Select one:
a. line of best fit.
b. line of central limit.
c. prediction estimate.
d. error of estimate.
Question 20
Once we have an equation for a straight line through our data, we can:
Select one:
a. use an independent-samples t test to compare means.
b. speculate about the causal relationship.
c. compute post hoc tests.
d. look at each value on the x-axis and predict its corresponding value on the y-axis.
